System-Layer Failure: Drift, Capacity Collapse, and AI as Compensatory Governance

System‑Layer Failure: Drift, Capacity Collapse, and AI as Compensatory Governance formalizes how institutional systems degrade over time and why artificial intelligence emerges precisely at the moment governance becomes structurally unsustainable. The paper positions itself as the historical and governance‑layer confirmation within the SignalRupture architecture, noting that it “completes the sequence by demonstrating how these dynamics unfold across real systems over time” and that it is “not a standalone analysis, but a proof layer that situates the SR framework within observable historical and institutional trajectories.” The paper introduces a drift–capacity model in which institutions accumulate complexity faster than they can process it, leading to over‑extraction of hidden buffers such as “surplus labor capacity… discretionary compliance… [and] temporal elasticity.” When these buffers collapse, systems do not fail abruptly; instead, “systems do not become dysfunctional; they become visible when the buffers masking their limitations are exhausted.” At this visibility threshold, AI appears not as innovation but as compensatory infrastructure. Through historical analysis—from early cybernetics to 1990s welfare automation to contemporary algorithmic governance—the paper demonstrates that AI adoption consistently follows periods of administrative overload, capacity strain, and institutional drift. It shows that AI functions as a stabilizing layer when institutions can no longer govern complexity directly.